In recent years, a significant number of manufacturing enterprises globally have adopted Enterprise Resource Planning (ERP) systems as a strategic step toward digital transformation, leveraging advancements in cloud-based technologies. ERP systems, characterized by their comprehensive database structures, support advanced capabilities such as Artificial Intelligence (AI), Big Data analytics, Machine Learning (ML), and process automation. Given their integrative potential, these systems effectively consolidate essential business functions, including Sales, Accounting, Manufacturing, Human Resources, and overall management.

Data quality emerges as a critical factor and one of the foundational pillars for the successful implementation of ERP systems. The relevance of high-quality data in ERP deployments is underscored by its direct influence on operational efficiency, departmental integration, and informed decision-making at executive levels. Poor data quality during ERP implementation can result in significant adverse effects, disrupting interdepartmental coordination, and leading to flawed strategic decisions.

This review addresses key data quality issues commonly encountered during the data migration phase, transitioning from legacy systems to modern ERP infrastructures. It highlights prominent data quality challenges, including data inconsistencies, duplication, incompleteness, and misalignment across disparate data sources. Additionally, the paper explores various methodologies and best practices for enhancing data quality, such as rigorous data cleansing, robust governance frameworks, and systematic validation procedures during migration.

Furthermore, this study emphasizes the criticality of maintaining data integrity throughout ERP implementation phases and identifies effective ERP project management practices as vital to ensuring successful system deployment. Insights drawn from recent literature and empirical case studies illustrate the strategies employed to mitigate data quality risks, ensuring the realization of anticipated ERP system benefits.

Data Quality, Data Integrity, Quality Control, ERP

NetSuite Inc. (2014). NetSuite products. Retrieved November 22, 2014, from http://www.netsuite.com/portal/resource/articles/erp/what-is-erp.shtml

Rothlin, M. (2014). An exploratory study of data quality management practices. Retrieved November 20, 2014, from http://books.google.lk/books?id=wyheQllcyh0Cpg=PA292lpg=M.Rothlin

El-Rayyes, E. K., & Abu-Zaid, I. M. (2012). New model to achieve software quality assurance (SQA) in web applications. *International Journal of Science and Technology, 2*(7).

Venkitaraman, R. (n.d.). Software quality assurance. Department of Computer Science, The University of Texas, Dallas.

Core. (n.d.). Retrieved from https://core.ac.uk/download/pdf/235049621.pdf

Liepins, G. E. (1989). Sound data are a sound investment. *Quality Progress, 22*(2), 61–64.

Loshin, D. (2009). *Master data management*. Elsevier Inc.

Pipino, L., Wang, R., Kopcso, D., & Rybolt, W. (2005). Developing a measurement scale for data quality dimensions in information quality. In V. Zwass (Ed.), *Advances in management information systems* (pp. 37–51). M. E. Sharpe Inc.

Kerr, K., Norris, T., & Stockdale, S. (2007). Data quality information and decision-making: A healthcare case study. In *Proceedings of the 18th Australasian Conference on Information Systems* (pp. 1017–1026), Toowoomba.

Batini, C., & Scannapieco, M. (2006). *Data quality concepts, methodologies, and techniques*. Springer.

Olson, J. E. (2003). *Data quality: The accuracy dimension*. Morgan Kaufmann Publishers.

Fisher, C. W., & Kingma, B. R. (2001). Criticality of data quality as exemplified in two disasters. *Information and Management, 39*(2), 109–116.